Bayes, Rationality, and Rashionality

This chapter reviews the main ideas of Bayesian approaches to learning, compared to associationist approaches. It reviews and discusses Bayesian criticisms of associationist learning theory. In particular, Bayesian theorists have argued that associative models fail to represent confidence in belief and update confidence with experience. The chapter discusses whether updating confidence is necessary to capture entrenchment, suspicious coincidence, and category variability effects. The evidence is argued to be somewhat inconclusive at present, as simulated annealing can often suffice. Furthermore, when confidence updating is suggested by the data, the updating suggested by the data may be non-normative, contrary to the Bayesian notion of the learner as an ideal observer. Following Kruschke, learned selective attention is argued to explain many ways in which human learning departs from that of the ideal observer, most crucially including the weakness of backward relative to forward blocking. Other departures from the ideal observer may be due to biological organisms taking into account factors other than belief accuracy. Finally, generative and discriminative learning models are compared. Generative models are argued to be particularly likely when active learning is a possibility and when reversing the observed mappings may be required.